Best Hugo Bowne Anderson Podcasts (2025)

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Episode 59: Patterns and Anti-Patterns For Building with AI 47:37

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9h ago47:37

47:37

John Berryman (Arcturus Labs; early GitHub Copilot engineer; co-author of Relevant Search and Prompt Engineering for LLMs) has spent years figuring out what makes AI applications actually work in production. In this episode, he shares the “seven deadly sins” of LLM development — and the practical fixes that keep projects from stalling. From context…

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Episode 24: Rebuilding an Airline for the 21st Century: LATAM's Data-Driven Transformation 49:56

10d ago49:56

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Andrés Bucchi (Chief Data Officer, LATAM Airlines) joins High Signal to unpack how a century-old airline reinvented itself with data and AI—and how that transformation is unlocking value from fuel efficiency to fraud detection. LATAM has built a massive data operation, experimenting across everything from pricing to operations, while customers bene…

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Episode 58: Building GenAI Systems That Make Business Decisions with Thomas Wiecki (PyMC Labs) 1:00:45

15d ago1:00:45

1:00:45

While most conversations about generative AI focus on chatbots, Thomas Wiecki (PyMC Labs, PyMC) has been building systems that help companies make actual business decisions. In this episode, he shares how Bayesian modeling and synthetic consumers can be combined with LLMs to simulate customer reactions, guide marketing spend, and support strategy. …

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Episode 23: Why Most AI Agents Fail (and What It Takes to Reach Production) 51:17

23d ago51:17

51:17

Anu Bharadwaj (President, Atlassian) joins High Signal to unpack how humans and AI agents will work together across the enterprise, and how that shift could change the very nature of teamwork. Atlassian employees have already built thousands of agents across product, marketing, engineering, and HR teams, while customers like HarperCollins are cutti…

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Episode 57: AI Agents and LLM Judges at Scale: Processing Millions of Documents (Without Breaking the Bank) 41:27

27d ago41:27

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While many people talk about “agents,” Shreya Shankar (UC Berkeley) has been building the systems that make them reliable. In this episode, she shares how AI agents and LLM judges can be used to process millions of documents accurately and cheaply. Drawing from work on projects ranging from databases of police misconduct reports to large-scale cust…

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Episode 22: Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It) 46:50

1M ago46:50

46:50

Tomasz Tunguz (Theory Ventures) joins High Signal to unpack why a trillion dollars of market cap is up for grabs as AI reshapes enterprise software. He explains why workflows are now changing faster than packaged software can keep up, how “liquid software” is redefining CRM and marketing automation, and why background agents will require a new kind…

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Episode 56: DeepMind Just Dropped Gemma 270M... And Here’s Why It Matters 45:40

1M ago45:40

45:40

While much of the AI world chases ever-larger models, Ravin Kumar (Google DeepMind) and his team build across the size spectrum, from billions of parameters down to this week’s release: Gemma 270M, the smallest member yet of the Gemma 3 open-weight family. At just 270 million parameters, a quarter the size of Gemma 1B, it’s designed for speed, effi…

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Episode 55: From Frittatas to Production LLMs: Breakfast at SciPy 38:08

1M ago38:08

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Traditional software expects 100% passing tests. In LLM-powered systems, that’s not just unrealistic — it’s a feature, not a bug. Eric Ma leads research data science in Moderna’s data science and AI group, and over breakfast at SciPy we explored why AI products break the old rules, what skills different personas bring (and miss), and how to keep sy…

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Episode 21: Why Great Data Still Leads to Bad Decisions (And How to Fix It) 50:38

2M ago50:38

50:38

Amy Edmondson (Harvard Business School) and Mike Luca (Johns Hopkins) join High Signal to unpack what actually drives good decisions in data‑rich organizations. Using contrasts like the Bay of Pigs vs. the Cuban Missile Crisis and product cases such as Airbnb’s work on measuring discrimination, they show how decision quality tracks conversation qua…

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Episode 20: Incentives, Accountability, and the Data Leader’s Dilemma 1:03:13

2M ago1:03:13

1:03:13

Daragh Sibley, Chief Algorithms Officer at Literati and former Director of Data Science at Stitch Fix, joins High Signal to unpack how machine-learning moves from slide-deck promise to bottom-line impact. He walks through his shift from academic research on how kids learn to read to owning inventory and personalization algorithms that decide which …

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Episode 54: Scaling AI: From Colab to Clusters — A Practitioner’s Guide to Distributed Training and Inference 41:17

2M ago41:17

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Colab is cozy. But production won’t fit on a single GPU. Zach Mueller leads Accelerate at Hugging Face and spends his days helping people go from solo scripts to scalable systems. In this episode, he joins me to demystify distributed training and inference — not just for research labs, but for any ML engineer trying to ship real software. We talk t…

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Episode 53: Human-Seeded Evals & Self-Tuning Agents: Samuel Colvin on Shipping Reliable LLMs 44:49

3M ago44:49

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Demos are easy; durability is hard. Samuel Colvin has spent a decade building guardrails in Python (first with Pydantic, now with Logfire), and he’s convinced most LLM failures have nothing to do with the model itself. They appear where the data is fuzzy, the prompts drift, or no one bothered to measure real-world behavior. Samuel joins me to show …

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Episode 19: Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI 54:08

3M ago54:08

54:08

Lis Costa, Chief of Innovation and Partnerships at the Behavioural Insights Team, joins High Signal to explore how behavioral science is reshaping public policy, digital platforms, and machine learning. She explains how defaults influence behavior at scale, why personalization and chatbots are unlocking new kinds of interventions, and what happens …

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Episode 52: Why Most LLM Products Break at Retrieval (And How to Fix Them) 28:38

3M ago28:38

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Most LLM-powered features do not break at the model. They break at the context. So how do you retrieve the right information to get useful results, even under vague or messy user queries? In this episode, we hear from Eric Ma, who leads data science research in the Data Science and AI group at Moderna. He shares what it takes to move beyond toy dem…

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Episode 51: Why We Built an MCP Server and What Broke First 47:41

3M ago47:41

47:41

What does it take to actually ship LLM-powered features, and what breaks when you connect them to real production data? In this episode, we hear from Philip Carter — then a Principal PM at Honeycomb and now a Product Management Director at Salesforce. In early 2023, he helped build one of the first LLM-powered SaaS features to ship to real users. M…

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Episode 18: High-Stakes AI Systems and the Cost of Getting It Wrong 58:45

3M ago58:45

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Sudarshan Seshadri—VP of AI, Data Science, and Foundations Engineering at Alto Pharmacy—joins us to explore what it takes to build high-stakes AI systems that people can actually trust. He shares lessons from deploying machine learning and LLMs in healthcare, where speed, safety, and uncertainty must be carefully balanced. We talk about designing A…

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Episode 50: A Field Guide to Rapidly Improving AI Products -- With Hamel Husain 27:42

3M ago27:42

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If we want AI systems that actually work, we need to get much better at evaluating them, not just building more pipelines, agents, and frameworks. In this episode, Hugo talks with Hamel Hussain (ex-Airbnb, GitHub, DataRobot) about how teams can improve AI products by focusing on error analysis, data inspection, and systematic iteration. The convers…

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Episode 49: Why Data and AI Still Break at Scale (and What to Do About It) 1:21:45

4M ago1:21:45

1:21:45

If we want AI systems that actually work in production, we need better infrastructure—not just better models. In this episode, Hugo talks with Akshay Agrawal (Marimo, ex-Google Brain, Netflix, Stanford) about why data and AI pipelines still break down at scale, and how we can fix the fundamentals: reproducibility, composability, and reliable execut…

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Episode 17: The Incentive Problem in Shipping AI Products — and How to Change It 53:52

4M ago53:52

53:52

Roberto Medri, VP of Data Science at Instagram, explains why most experiments fail, how misaligned incentives warp product development, and what it takes to drive real impact with data science. He shares what teams get wrong about launches, why ego gets in the way of learning, and how Instagram turned Reels from a struggling product into a global s…

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Episode 48: HOW TO BENCHMARK AGI WITH GREG KAMRADT 1:04:25

4M ago1:04:25

1:04:25

If we want to make progress toward AGI, we need a clear definition of intelligence—and a way to measure it. In this episode, Hugo talks with Greg Kamradt, President of the ARC Prize Foundation, about ARC-AGI: a benchmark built on Francois Chollet’s definition of intelligence as “the efficiency at which you learn new things.” Unlike most evals that …

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Episode 16: How Human-Centered AI Actually Gets Built 47:22

4M ago47:22

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Fei-Fei Li—co-director of Stanford’s Human-Centered AI Institute and one of the most respected voices in the field—reflects on AI’s evolution from the early days of ImageNet to the rise of foundation models. She explains why spatial intelligence may be the next major shift, how human-centered design applies in practice, and why AI should be underst…

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Episode 15: Why Good Metrics Still Lead to Bad Decisions — and How to Fix It 54:17

5M ago54:17

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Eoin O'Mahony—data science partner at Lightspeed, former Uber science lead, and one of the early architects of the system that kept NYC’s Citi Bikes available across the city—argues that positive metrics are meaningless if you don’t understand the mechanism behind them. At Uber, he was careful to make sure his launches both looked good on paper and…

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Episode 14: Why Most Companies Aren’t Actually AI Ready (and What to Do About It) 51:58

6M ago51:58

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Barr Moses—co-founder and CEO of Monte Carlo—thinks we’re headed for an AI reckoning. Companies are building fast, but most are still managing data like it’s 2015. In this episode, she shares high-stakes failure stories (like a $100M schema change), explains why full-stack observability is becoming essential, and breaks down how LLM agents are alre…

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Episode 47: The Great Pacific Garbage Patch of Code Slop with Joe Reis 1:19:12

6M ago1:19:12

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What if the cost of writing code dropped to zero — but the cost of understanding it skyrocketed? In this episode, Hugo sits down with Joe Reis to unpack how AI tooling is reshaping the software development lifecycle — from experimentation and prototyping to deployment, maintainability, and everything in between. Joe is the co-author of Fundamentals…

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Episode 46: Software Composition Is the New Vibe Coding 1:08:57

6M ago1:08:57

1:08:57

What if building software felt more like composing than coding? In this episode, Hugo and Greg explore how LLMs are reshaping the way we think about software development—from deterministic programming to a more flexible, prompt-driven, and collaborative style of building. It’s not just hype or grift—it’s a real shift in how we express intent, reaso…

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Episode 13: The End of Programming As We Know It 1:23:09

6M ago1:23:09

1:23:09

Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what …

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Episode 12: Your Machine Learning Solves The Wrong Problem 54:40

7M ago54:40

54:40

Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? St…

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Episode 11: What Comes After Code? The Role of Engineers in an AI-Driven Future 1:05:44

7M ago1:05:44

1:05:44

Peter Wang—Chief AI Officer at Anaconda and a driving force behind PyData—challenges conventional thinking about AI’s role in software development. As AI reshapes engineering, are we moving beyond writing code to orchestrating intelligence? Peter explores why companies are fixated on models instead of integration, how AI is breaking traditional sof…

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Episode 45: Your AI application is broken. Here’s what to do about it. 1:17:30

7M ago1:17:30

1:17:30

Too many teams are building AI applications without truly understanding why their models fail. Instead of jumping straight to LLM evaluations, dashboards, or vibe checks, how do you actually fix a broken AI app? In this episode, Hugo speaks with Hamel Husain, longtime ML engineer, open-source contributor, and consultant, about why debugging generat…

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Episode 10: AI Won't Save You But Data Intelligence Will 59:42

7M ago59:42

59:42

Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI …

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Episode 44: The Future of AI Coding Assistants: Who’s Really in Control? 1:34:11

8M ago1:34:11

1:34:11

AI coding assistants are reshaping how developers write, debug, and maintain code—but who’s really in control? In this episode, Hugo speaks with Tyler Dunn, CEO and co-founder of Continue, an open-source AI-powered code assistant that gives developers more customization and flexibility in their workflows. In this episode, we dive into: The trade-of…

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Episode 9: Why 90% of Data Science Fails—And How to Fix It -- With Eric Colson 1:09:40

8M ago1:09:40

1:09:40

In this episode of High Signal, Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—breaks down why most companies fail to unlock the full potential of their data science teams. Drawing from years of experience leading data functions at top tech companies, Eric shares how organizations ca…

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Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production 1:01:03

8M ago1:01:03

1:01:03

Hugo speaks with Alex Strick van Linschoten, Machine Learning Engineer at ZenML and creator of a comprehensive LLMOps database documenting over 400 deployments. Alex's extensive research into real-world LLM implementations gives him unique insight into what actually works—and what doesn't—when deploying AI agents in production. In this episode, we …

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Episode 8: From Zero to Scale: Lessons from Airbnb and Beyond 1:06:42

9M ago1:06:42

1:06:42

In this episode of High Signal, Elena Grewal—former Head of Data Science at Airbnb, political consultant, professor at Yale, and ice cream shop owner—shares her journey of building data teams that scale across vastly different contexts. Drawing on her experiences in tech, consulting, and brick-and-mortar, Elena offers practical lessons on leadershi…

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Episode 42: Learning, Teaching, and Building in the Age of AI 1:20:03

9M ago1:20:03

1:20:03

In this episode of Vanishing Gradients, the tables turn as Hugo sits down with Alex Andorra, host of Learning Bayesian Statistics. Hugo shares his journey from mathematics to AI, reflecting on how Bayesian inference shapes his approach to data science, teaching, and building AI-powered applications. They dive into the realities of deploying LLM app…

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Episode 41: Beyond Prompt Engineering: Can AI Learn to Set Its Own Goals? 43:51

9M ago43:51

43:51

Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or questioning the hype, this episode dives into the tough…

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Episode 40: What Every LLM Developer Needs to Know About GPUs 1:43:34

9M ago1:43:34

1:43:34

Hugo speaks with Charles Frye, Developer Advocate at Modal and someone who really knows GPUs inside and out. If you’re a data scientist, machine learning engineer, AI researcher, or just someone trying to make sense of hardware for LLMs and AI workflows, this episode is for you. Charles and Hugo dive into the practical side of GPUs—from running inf…

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Episode 7: What Lies Beyond Machine Learning and AI: Decision Systems and the Future of Data Teams 1:18:44

9M ago1:18:44

1:18:44

In this episode of High Signal, Chris Wiggins—Chief Data Scientist at The New York Times, Professor at Columbia University, and co-author of How Data Happened—shares how organizations can move beyond prediction to actionable decision systems. Drawing on his work at The New York Times and in academia, Chris explains how to scale data teams, optimize…

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Episode 6: What Happens to Data Science in the Age of AI? 1:18:23

10M ago1:18:23

1:18:23

In this episode of High Signal, Hilary Mason—renowned data scientist, entrepreneur, and co-founder of Hidden Door—shares her unique insights into the evolving world of data science and generative AI. Drawing from her pioneering work at Fast Forward Labs, Bitly, and Hidden Door, Hilary explores how creativity, judgment, and empathy are reshaping the…

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Episode 39: From Models to Products: Bridging Research and Practice in Generative AI at Google Labs 1:43:28

10M ago1:43:28

1:43:28

Hugo speaks with Ravin Kumar,*Senior Research Data Scientist at Google Labs. Ravin’s career has taken him from building rockets at SpaceX to driving data science and technology at Sweetgreen, and now to advancing generative AI research and applications at Google Labs and DeepMind. His multidisciplinary experience gives him a rare perspective on bui…

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Episode 5: The Hard Truth About Building AI Systems and What Most Leaders Miss About AI 1:02:06

10M ago1:02:06

1:02:06

In this episode of High Signal, Gabriel Weintraub (the Amman Professor of Operations, Information, and Technology at Stanford Graduate School of Business), brings his expertise in market design, data science, and operations, enriched by his experience with global platforms like Uber and Mercado Libre, to a conversation that spans practical strategi…

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Episode 4: How to Build an Experimentation Machine and Where Most Go Wrong 51:16

11M ago51:16

51:16

Ramesh Johari (Stanford, Uber, Airbnb, and more) explores the art and science of online experimentation, especially in the context of marketplaces and tech companies. Ramesh shares insights on how organizations evolve from basic experimentation practices to becoming fast, adaptive, and self learning organizations. We dive into challenges like the r…

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Episode 38: The Art of Freelance AI Consulting and Products: Data, Dollars, and Deliverables 1:23:47

11M ago1:23:47

1:23:47

Hugo speaks with Jason Liu, an independent AI consultant with experience at Meta and Stitch Fix. At Stitch Fix, Jason developed impactful AI systems, like a $50 million product similarity search and the widely adopted Flight recommendation framework. Now, he helps startups and enterprises design and deploy production-level AI applications, with a f…

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Episode 3: Data Science Meets Management: Teamwork, Experimentation, and Decision-Making 52:12

11M ago52:12

52:12

Chiara Farronato (Harvard Business School) discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer ma…

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Episode 2: Fooling Yourself Less: The Art of Statistical Thinking in AI 1:00:51

11M ago1:00:51

1:00:51

Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causa…

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Episode 1: The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale 1:15:12

11M ago1:15:12

1:15:12

Michael Jordan (UC Berkeley) on the future of machine learning as it extends to a planetary scale in "The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale." In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI ca…

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Episode 37: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 2 50:36

12M ago50:36

50:36

Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton s…

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Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1 1:03:46

12M ago1:03:46

1:03:46

Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational linguistics; and Dennis Peskoff, a researcher from Princeton s…

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Episode 35: Open Science at NASA -- Measuring Impact and the Future of AI 58:13

1y ago58:13

58:13

Hugo speaks with Dr. Chelle Gentemann, Open Science Program Scientist for NASA’s Office of the Chief Science Data Officer, about NASA’s ambitious efforts to integrate AI across the research lifecycle. In this episode, we’ll dive deeper into how AI is transforming NASA’s approach to science, making data more accessible and advancing open science pra…

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Episode 34: The AI Revolution Will Not Be Monopolized 1:42:51

1y ago1:42:51

1:42:51

Hugo speaks with Ines Montani and Matthew Honnibal, the creators of spaCy and founders of Explosion AI. Collectively, they've had a huge impact on the fields of industrial natural language processing (NLP), ML, and AI through their widely-used open-source library spaCy and their innovative annotation tool Prodigy. These tools have become essential …